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Data Analysis and Knowledge Discovery  2019, Vol. 3 Issue (10): 78-88    DOI: 10.11925/infotech.2096-3467.2019.0052
Current Issue | Archive | Adv Search |
Cross-media Fusion Method Based on LDA2Vec and Residual Network
Qinghong Zhong1,2,Xiaodong Qiao1,Yunliang Zhang1,2(),Mengjuan Weng1,2
1Institute of Scientific and Technical Information of China, Beijing 100038, China
2Key Laboratory of Rich-media Knowledge Organization and Service of Digital Publishing Content, Beijing 100038, China
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Abstract  

[Objective] This paper optimizes feature extraction based on the theory of cross-media fusion mechanism, aiming to reduce the semantic gaps between heterogeneous data. [Methods] With the help of LDA2Vec and ResNet V2 models, we extracted features from the texts and images. Then, we used semantic association matching technique to map the heterogeneous text / image features to the consistent expression space. [Results] Compared with the performance of the LDA and SIFT algorithms, the proposed method increased the MAP value of text / image mutual retrieval to 0.454. [Limitations] The size of training sets needs to be expanded and extracting the optimization features has limited impacts on cross-media fusion. [Conclusions] The proposed method is effective and provides new directions for cross-media studies.

Key wordsCross-media Data      Feature Extraction      LDA2Vec      ResNet      Semantic Association     
Received: 14 January 2019      Published: 25 November 2019
ZTFLH:  G354  
Corresponding Authors: Yunliang Zhang     E-mail: zhangyl@istic.ac.cn

Cite this article:

Qinghong Zhong,Xiaodong Qiao,Yunliang Zhang,Mengjuan Weng. Cross-media Fusion Method Based on LDA2Vec and Residual Network. Data Analysis and Knowledge Discovery, 2019, 3(10): 78-88.

URL:

http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2019.0052     OR     http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2019/V3/I10/78

研究方法 优点 缺点
基于统计的方法 TF、DF、TF-IDF[10] 用词出现频率来衡量其重要性, 易于理解,
突出重要单词, 实现难度小
仅考虑词频不能有效反映词汇的重要程度
以及分布情况
信息增益(IG)[11] 选择最大的信息增益属性进行划分, 能很
好地衡量出文档分类效果
其结果会偏向取值较多的特征
互信息(MI)[12] 依据每个特征项与各个类别的平均关联程
度选择出一定量的特征项
容易选出生僻词甚至是噪音词, 这些词的
MI大但是携带较少的类别信息
基于词向量的方法 Word2Vec[13] 包含语义信息, 一定程度上解决了语义鸿
沟问题, 高效
需要大量训练语料, 具有不可解释性
基于主题模型的方法 LDA[14] 找到没有共同词的两篇文档之间的潜在联
系、挖掘出文档中的潜在词
受文档长度限制, 短文本无法挖掘出有用
的词
LDA2Vec[15] 吸收了Word2Vec局部预测和LDA全局预
测的优点, 在单词和文档上构建表示
训练时间长, 同样受文档长度影响
研究方法 优点 缺点
视觉底层
特征提取
颜色特征 RGB、HIS、HSV、
CMYK[17]
对图片大小、方向都不敏感, 在一些情况下
表现出相当强的鲁棒性
只用颜色特性很难完整而准确地描述一个具体物体, 不适合人的视觉特点
形状特征 SIFT特征提取算
[18]
适用于不变特征的提取, 提取的特征比较
稳定, 具有较好的区分性, 信息量丰富, 适
用于海量特征数据的处理
特征描述符的维数过大以及耗时过长, 实时性不高, 对边缘光滑的目标无法准确提取特征点
纹理特征 灰度共生矩阵、
随机场模型方法、
小波变换
反映了图像中的同质现象, 具有旋转不变
性以及良好的抗噪性
容易受分辨率变化、光照变化等的影响
空间关系
特征
姿态估计 可加强对图像内容的描述区分能力 对图像目标的旋转、图像目标的反转以及尺度变化都较为敏感
神经网络方法 卷积神经网络
CNN[19]
无需手动选取特征, 对高维数据的处理
无压力, 尤其对图像这种高度非结构化、分
布复杂的数据具有很强的处理能力
需要大量的训练数据、对计算计性能要求高
实验 映射方法 Image to Text Text to Image Avg
文献[5]
LDA
CCA 0.249 0.196 0.223
SM 0.225 0.223 0.224
SCM 0.277 0.226 0.252
LDA2Vec
Epoch=5
CCA 0.243 0.198 0.221
SM 0.298 0.228 0.263
SCM 0.283 0.227 0.255
LDA2Vec
Epoch=10
CCA 0.244 0.199 0.222
SM 0.302 0.228 0.265
SCM 0.288 0.233 0.261
LDA2Vec
Epoch=15
CCA 0.244 0.199 0.222
SM 0.301 0.224 0.263
SCM 0.291 0.225 0.258
LDA2Vec
Epoch=20
CCA 0.244 0.199 0.222
SM 0.301 0.223 0.262
SCM 0.287 0.224 0.256
LDA2Vec
Epoch=25
CCA 0.245 0.198 0.222
SM 0.298 0.225 0.262
SCM 0.284 0.226 0.255
Topic1 Topic2 Topic3 Topic4 Topic5 Topic6 Topic7 Topic8 Topic9 Topic10
king book water empire attack park government album game specie
queen publish whale roman japanese build party band team animal
prince story specie emperor air creek country release win cell
son fiction bird french command building national music match dinosaur
george poem north military navy town india song league bird
duke literature range government german river school studio player bone
family letter tree germany battle railway economic episode club skull
jame life forest battle fire centre university scene score male
william writer nest power aircraft road people production test study
henry author population treaty gun west public star final prey
Royalty &
Nobility
Literature& Theater / Warfare Art &
Architecture
/ Music Sport &
Recreation
Biology
实验 映射方法 Image to Text Text to Image Avg
文献[5]
SIFT
CCA 0.249 0.196 0.223
SM 0.225 0.223 0.224
SCM 0.277 0.226 0.252
ResNet V2
256维
CCA 0.417 0.410 0.414
SM 0.435 0.379 0.407
SCM 0.454 0.449 0.452
ResNet V2
2 048维
CCA 0.392 0.379 0.386
SM 0.415 0.366 0.391
SCM 0.437 0.437 0.437
实验 文本特征提取方法 图片特征提取方法 映射方法 Image to Text Text to Image Avg
文献[5] LDA SIFT SCM 0.277 0.226 0.252
文献[31] LDA CNN-Fc6 SCM 0.417 0.352 0.385
文献[9] LDA CNN-Fc7 SM 0.430 0.370 0.400
文献[27] TextNet CNN-Fc8 Deep-SM 0.478 0.422 0.450
文献[26] / / DCCA 0.445 0.399 0.422
4.2节 LDA2Vec SIFT SM 0.302 0.228 0.265
4.3节 LDA ResNet V2 256维 SCM 0.454 0.449 0.452
LDA2Vec+ResNet V2 256维 LDA2Vec ResNet V2 256维 CCA 0.418 0.411 0.415
SM 0.445 0.375 0.410
SCM 0.462 0.446 0.454
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